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While developing for multiple platforms at once seems like a convenient solution, there are several challenges arising when trying to abstract the entire mobile development. This paper is meant to evaluate current cross-platform development for mobile applications. The background for its necessity, its conceptual approach and the problems to face when developing cross-platform were determined and explained in detail. Afterwards, certain solutions were evaluated against the former insights. Based on the results, an informed discussion and conclusion was performed. The mobile environment consist of only two big players by now, Android and iOS. These operating systems differ in architecture, design and consequently in the way applications are developed for each of them. Therefore, high demands are made towards cross-platform solutions. Tools which allow for the creation of applications for multiple platforms at once have to match native applications in regard to user experience and performance. At the same time they need to be able to optimize developments with the goal of being cost efficient. Apache Cordova, Xamarin and NativeScript were selected for evaluation in regard to their ability to meet these requirements. Cordova acts as the comparison group of cross-platform tools. It is the big player in the field and there are reasons for this. However, aspiring solutions with higher nativity and ambitious approaches are emerging. Xamarin and NativeScript deliver top quality results while offering loosely coupled developments. Therefore it is possible to develop high quality applications and still benefit from the advantages of platform-independent solutions. As a consequence mobile development is about to change in the foreseeable future. More sophisticated approaches may lead to a higher number of developments done cross-platform, and rightfully so.
Investing as Random Trial
(2017)
We introduce an investment algorithm for a market of individual securities. The investment algorithm is derived from constraints depending on investment parameters in order to limit the risk and to take into account an individual investor. One constraint is devoted to trading costs. Purchased securities are selected randomly among securities that meet the buy condition, making trading a random trial. Simulations with historical price data are demonstrated for a simple example: The buy condition is evaluated on the basis of the price relationship for two subsequent trading days and the sales condition is defined by holding securities only for one day. A trading expert evaluates the expected return for the investment algorithm with respect to the random selection. Thus, the expert informs precisely on how many market players perform using the same investment algorithm. Its findings are for a parametrized set of buy conditions simultaneously, which makes a trading expert a valuable tool for theorists as well as for practitioners. In our example, the trading expert demonstrated clearly a significant mean reversion effect for a horizon of one day.